CNV And Stroke (CaNVAS)

CNV 和中风 (CaVAS)

基本信息

批准号：
10308412
负责人：
JOHN W. COLE
金额：
$ 50.73万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10308412
关键词：
Address Adult Affect African Age Bioinformatics Biological Biological Markers Blood Blood Pressure Cardiovascular system Cause of Death Coagulation Process Complex Copy Number Polymorphism Data Data Set Detection Disease Drug Targeting Ethnic Origin Etiology European Functional disorder Genes Genetic Genetic study Genotype Heritability Inflammation Interdisciplinary Study International Ischemic Stroke Leadership London Manuals Measures Messenger RNA Methodology Methylation MicroRNAs Morbidity - disease rate Outcome Pathway interactions Phenotype Population Positioning Attribute Predisposition Prevention Proteomics RNA Research Research Personnel Resources Risk Risk Factors Role Stroke Stroke prevention Susceptibility Gene Sweden Syndrome Training Trans-Omics for Precision Medicine Treatment outcome United States Variant Woman base case control causal variant circulating biomarkers cost cost efficient disability ethnic diversity exome experience genome wide association study genome-wide improved insight men metabolomics mortality novel programs sex stroke outcome stroke risk tool

项目摘要

Ischemic stroke is the 4th leading cause of death in the U.S. and a major cause of disability. The etiology of stroke is multifactorial and poorly understood. Genetics is a potentially powerful tool for better understanding disease etiology as it can highlight biological mechanisms underlying disease and point the way to improved prevention, treatment, and outcome. Large genome-wide association studies (GWAS) of ischemic stroke (IS) populations have been successful at identifying stroke-risk-associated loci with small effect sizes, however, the role of copy number variation (CNV) variation in stroke susceptibility has yet to be explored, and is the premise of our proposal. Studying CNV has revealed important insights for numerous other complex diseases. Further, we recently demonstrated that a higher CNV burden genome-wide is associated with poorer stroke outcome at 3 months. We therefore hypothesize that CNV analyses of existing GWAS and exome data will be a highly effective and cost-efficient methodology to identify novel associations illuminating stroke mechanisms, treatment targets, and outcome drivers. We further speculate that these analyses will identify CNVs of large effect size in ischemic stroke, as suggested by the existence of numerous monogenic, syndromic and complex diseases associated with CNV and that CNV may help explain the ‘missing heritability’ known to exist in stroke. For this application, we have already assembled over 24,500 well-phenotyped IS cases, including IS subtypes, and over 43,500 controls, all with readily available genotyping on GWAS and exome arrays, with case measures of stroke outcome. To evaluate CNV-associated stroke risk and stroke outcome we will: 1) perform Risk Discovery using several analytic approaches to identify CNVs that are associated with the risk of IS and its subtypes, across the age-, sex- and ethnicity-spectrums; 2) perform Risk Replication and Extension to determine whether the identified stroke-associated CNVs replicate in the ethnically diverse TOPMed Consortia and then using existing TOPMed and GeneStroke Consortium biomarker data (e.g. methylation, proteomic, RNA, miRNA, etc.) evaluate how the identified CNVs exert their effects on stroke risk, and lastly; 3) perform outcome-based Replication and Extension analyses of our recent findings demonstrating an inverse relationship between CNV burden and stroke outcome at 3 months (mRS) in these additional datasets, and then determine the key CNV drivers responsible for these associations using existing biomarker data. Our study will leverage the numerous advantages of using existing case-control data sets, exploring the relationships between CNV and IS and its subtypes, and outcome at 3 months, across the sex-, age- and ethnicity-spectrums. The proposed study creates a new training network for junior investigators and establishes a unique resource for the continued study of the genetic basis of IS. The successful identification of novel genes, pathways and drug targets has the potential to transform our understanding of the stroke pathophysiology leading to more effective prevention, treatment and outcome strategies.

缺血性中风是美国第四大死亡原因，也是导致残疾的主要原因。中风的发生是多因素造成的，但人们对其知之甚少。遗传学是更好地理解的潜在强大工具。疾病病因学，因为它可以突出疾病潜在的生物学机制并指出改善的方法缺血性中风（IS）的大型全基因组关联研究（GWAS）。人群已成功识别出中风风险相关位点，且效应较小，但是，拷贝数变异（CNV）变异在中风易感性中的作用尚未被探索，并且是研究 CNV 揭示了许多其他复杂疾病的重要见解。此外，我们最近证明，全基因组范围内较高的 CNV 负担与较差的中风相关因此，我们发现现有 GWAS 和外显子组数据的 CNV 分析将是一种高效且具有成本效益的方法来识别阐明中风机制的新关联，我们进一步推测这些分析将识别大量的 CNV。缺血性中风的效应大小，如大量单基因、综合征和复合基因的存在所表明的那样与 CNV 相关的疾病，CNV 可能有助于解释中风中已知存在的“遗传性缺失”。对于此应用，我们已经组装了超过 24,500 个表型良好的 IS 病例，包括 IS 亚型，以及超过 43,500 个对照，所有这些都可以在 GWAS 和外显子组阵列上进行现成的基因分型，为了评估 CNV 相关的中风风险和中风结果，我们将： 1) 使用多种分析方法执行风险发现，以识别与风险相关的 CNV IS 及其亚型，跨越年龄、性别和种族范围 2) 进行风险复制和扩展；确定已识别的中风相关 CNV 是否在不同种族的 TOPMed 中复制联盟，然后使用现有的 TOPMed 和 GeneStroke Consortium 生物标志物数据（例如甲基化、蛋白质组学、RNA、miRNA 等）评估已识别的 CNV 如何对中风风险发挥作用，最后； 3）对我们最近的研究结果进行基于结果的复制和扩展分析，证明了相反的结果这些附加数据集中 CNV 负担与 3 个月时卒中结果 (mRS) 之间的关系，以及然后使用现有的生物标志物数据确定导致这些关联的关键 CNV 驱动因素。我们的研究将利用现有病例对照数据集的众多优势，探索 CNV 和 IS 及其亚型之间的关系，以及 3 个月的结果，跨性别、年龄和拟议的研究为初级研究人员创建了一个新的培训网络，并建立了一个新的培训网络。成功鉴定新型 IS 的遗传基础的独特资源。基因、通路和药物靶点有可能改变我们对中风的理解病理生理学导致更有效的预防、治疗和结果策略。